Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
NMR Biomed. 2022 Jul;35(7):e4718. doi: 10.1002/nbm.4718. Epub 2022 Mar 14.
The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [K ], fractional volume of the extravascular extracellular space [v ], and blood plasma volume fraction [v ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of K and v , and less than 11% in the estimation of v . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.
本研究旨在开发一种基于角度优化的径向稀疏并行磁共振成像（GRASP）的新型数据驱动的定量动态对比增强磁共振成像（DCE-MRI）技术，该技术具有高空间分辨率和高时间分辨率，且能直接从每位患者的数据中估算动脉输入函数（AIF），实现药代动力学（PK）分析。对 13 例妇科恶性肿瘤患者进行了 DCE-MRI 检查，使用 3T 磁共振扫描仪，采用单次连续的黄金角星状采集，并利用 GRASP 的独特功能，以用户定义的时间分辨率重建采集的数据，实现两种时间分辨率的图像重建。通过交替进行 AIF 和 PK 估计的迭代算法，联合估计 AIF（AIF 形状和延迟）和 PK 参数。通过计算机模拟确定估计参数的准确性（表示为百分比误差 [PE]）和精度。通过群体平均和数据驱动的 AIF 测量肿瘤对比动力学数据拟合误差的 PK 参数（容积转移常数 [K]、血管外细胞外间隙的分数容积[v]和血浆容积分数[v]）和归一化均方根误差 [nRMSE]（%）。在患者数据中，采用 Wilcoxon 符号秩检验比较 nRMSE。模拟结果表明，对于 AIF 估计，GRASP 图像重建的时间分辨率为 1 帧/秒，对于 PK 分析，时间分辨率为 5 帧/秒，K 和 v 的估计绝对 PE 小于 5%，v 的估计绝对 PE 小于 11%。双时相分辨率图像重建和数据驱动 AIF 的 nRMSE（平均值 ± SD）为 0.16 ± 0.04，而使用 1 帧/秒的群体平均 AIF 时为 0.27 ± 0.10（p < 0.001），使用 5 帧/秒的群体平均 AIF 时为 0.23 ± 0.07（p < 0.001）。综上所述，GRASP 技术采集和重建的双时相分辨率 DCE-MRI 数据可成功用于从单次采集联合估计 AIF 和 PK 参数，从而获得数据驱动的 AIF 和体素 PK 参数图。
